Early prediction of intestinal mucosal barrier function impairment by elevated serum procalcitonin in rats with severe acute pancreatitis

OBJECTIVES

The aim of this study was to evaluate serum procalcitonin (PCT) levels as a prognostic indicator of intestinal barrier function impairment in rats with severe acute pancreatitis (SAP).

METHODS

Thirty-six male Sprague Dawley rats were randomly grouped into SAP group (injected sodium taurocholate via biliopancreatic duct), Gln group (gavaged with glutamine after modeling), and control group. Blood, pancreatic, and terminal ileum tissues were obtained from the rats after 6 h of modeling. Serum amylase (Amy) levels were determined using an automatic biochemical detector, while endotoxin (ET), diamine oxidase (DAO), and PCT levels were measured by ELISA test. The pathology of pancreatic and small intestine tissues were observed. PCT protein expression in intestinal tissues were detected by immunohistochemistry and western blot.

RESULT

Pancreatic and intestinal injuries in Gln group were significantly lower than SAP group. Serum amylase, DAO, and PCT levels in SAP and Gln groups differed greatly and were significantly higher than control group. Immuno-histochemistry and western blot results showed that PCT protein expression levels in small intestine tissues of SAP group were higher than Gln group and control group. Serum PCT levels had a significant correlation with serum endotoxin, DAO levels and intestinal mucosal injury scores.

CONCLUSION

PCT expression in serum and intestinal tissues in SAP rats increased significantly in the early stages of SAP, and was closely related to the onset and degree of intestinal barrier function impairment. Thus, our results showed that measuring serum PCT can be used to predict intestinal mucosal barrier function impairment in SAP rats.

Are there new approaches for diagnosis, therapy guidance and outcome prediction of sepsis?

Beside many efforts to improve outcome, sepsis is still one of the most frequent causes of death in critically ill patients. It is the most common condition with high mortality in intensive care units. The complexity of the septic syndrome comprises immunological aspects - i.e., sepsis induced immunosuppression - but is not restricted to this fact in modern concepts. So far, exact mechanisms and variables determining outcome and mortality stay unclear. Since there is no typical risk profile, early diagnosis and risk stratification remain difficult, which hinders rapid and effective treatment initiation. Due to the heterogeneous nature of sepsis, potential therapy options should be adapted to the individual. Biomarkers like C-reactive protein and procalcitonin are routinely used as complementary tools in clinical decision-making. Beyond the acute phase proteins, a wide bunch of promising substances and non-laboratory tools with potential diagnostic and prognostic value is under intensive investigation. So far, clinical decision just based on biomarker assessment is not yet feasible. However, biomarkers should be considered as a complementary approach.

Quantitative forecasting of PTSD from early trauma responses: a Machine Learning application

There is broad interest in predicting the clinical course of mental disorders from early, multimodal clinical and biological information. Current computational models, however, constitute a significant barrier to realizing this goal. The early identification of trauma survivors at risk of post-traumatic stress disorder (PTSD) is plausible given the disorder's salient onset and the abundance of putative biological and clinical risk indicators. This work evaluates the ability of Machine Learning (ML) forecasting approaches to identify and integrate a panel of unique predictive characteristics and determine their accuracy in forecasting non-remitting PTSD from information collected within 10 days of a traumatic event. Data on event characteristics, emergency department observations, and early symptoms were collected in 957 trauma survivors, followed for fifteen months. An ML feature selection algorithm identified a set of predictors that rendered all others redundant. Support Vector Machines (SVMs) as well as other ML classification algorithms were used to evaluate the forecasting accuracy of i) ML selected features, ii) all available features without selection, and iii) Acute Stress Disorder (ASD) symptoms alone. SVM also compared the prediction of a) PTSD diagnostic status at 15 months to b) posterior probability of membership in an empirically derived non-remitting PTSD symptom trajectory. Results are expressed as mean Area Under Receiver Operating Characteristics Curve (AUC). The feature selection algorithm identified 16 predictors, present in ≥ 95% cross-validation trials. The accuracy of predicting non-remitting PTSD from that set (AUC = .77) did not differ from predicting from all available information (AUC = .78). Predicting from ASD symptoms was not better then chance (AUC = .60). The prediction of PTSD status was less accurate than that of membership in a non-remitting trajectory (AUC = .71). ML methods may fill a critical gap in forecasting PTSD. The ability to identify and integrate unique risk indicators makes this a promising approach for developing algorithms that infer probabilistic risk of chronic posttraumatic stress psychopathology based on complex sources of biological, psychological, and social information.

Early prediction of medication refractoriness in children with idiopathic epilepsy based on scalp EEG analysis

Refractory epilepsy often has deleterious effects on an individual's health and quality of life. Early identification of patients whose seizures are refractory to antiepileptic drugs is important in considering the use of alternative treatments. Although idiopathic epilepsy is regarded as having a significantly lower risk factor of developing refractory epilepsy, still a subset of patients with idiopathic epilepsy might be refractory to medical treatment. In this study, we developed an effective method to predict the refractoriness of idiopathic epilepsy. Sixteen EEG segments from 12 well-controlled patients and 14 EEG segments from 11 refractory patients were analyzed at the time of first EEG recordings before antiepileptic drug treatment. Ten crucial EEG feature descriptors were selected for classification. Three of 10 were related to decorrelation time, and four of 10 were related to relative power of delta/gamma. There were significantly higher values in these seven feature descriptors in the well-controlled group as compared to the refractory group. On the contrary, the remaining three feature descriptors related to spectral edge frequency, kurtosis, and energy of wavelet coefficients demonstrated significantly lower values in the well-controlled group as compared to the refractory group. The analyses yielded a weighted precision rate of 94.2%, and a 93.3% recall rate. Therefore, the developed method is a useful tool in identifying the possibility of developing refractory epilepsy in patients with idiopathic epilepsy.

Role of biomarkers in early detection of preeclampsia

Preeclampsia (PE) is a pregnancy-related, potentially life threatening condition. The incidence of PE has increased in the past decade, which has been attributed to various predisposing factors. Abnormal placentation is central to the evolution of this disease process. However, the triggering factor for this is still unknown. Interestingly, intense research done in this arena has unveiled the names of some important biomolecules which play important role in the vasculognesis of the early placenta, namely, vascular endothelial growth factor (VEGF) and placental growth factor (PlGF) and their antagonists, namely, soluble fms-like tyrosine kinase 1 (sFlt-1, also known as sVEGFR1), and soluble endoglin (sEng). Besides these, Renin Angiotensin System (RAS) was also implicated in this disease process. The roles of immune factors, genetic factors have been stressed from time to time. More novel approaches made, have shed light on the upcoming biomolecules. All these endeavours are directed to diagnose PE as early as possible, which is a real challenge. Question remains whether a single set parameters could diagnose a disease entity which is as complex as PE. Therefore, it is imperative to design feasible, predictive test-set utilizing multiple biomarkers.

Dimensionality reduced cortical features and their use in predicting longitudinal changes in Alzheimer's disease

Neuroimaging features derived from the cortical surface provide important information in detecting changes related to the progression of Alzheimer's disease (AD). Recent widespread adoption of neuroimaging has allowed researchers to study longitudinal data in AD. We adopted cortical thickness and sulcal depth, parameterized by three-dimensional meshes, from magnetic resonance imaging as the surface features. The cortical feature is high-dimensional, and it is difficult to use directly with a classifier because of the "small sample size" problem. We applied manifold learning to reduce the dimensionality of the feature and then tested the usage of the dimensionality reduced feature with a support vector machine classifier. Principal component analysis (PCA) was chosen as the method of manifold learning. PCA was applied to a region of interest within the cortical surface. We used 30 normal, 30 mild cognitive impairment (MCI) and 12 conversion cases taken from the ADNI database. The classifier was trained using the cortical features extracted from normal and MCI patients. The classifier was tested for the 12 conversion patients only using the imaging data before the actual conversion. The conversion was predicted early with an accuracy of 83%.

Preventive treatment of alveolar pulmonary edema of cardiogenic origin

OBJECTIVE

To evaluate the efficacy of preventive treatment (PT) on alveolar pulmonary edema (APE) of cardiogenic origin using a monitor based on principles of internal thoracic impedance (ITI) measurements.

METHODS

We conducted blinded clinical trials on patients with ST-elevation myocardial infarction (STEMI) and monitored whether the condition would progress to APE. ITI was measured non-invasively by the Edema Guard Monitor (EGM, model RS-207) every 30 min. The measurement threshold for the diagnosis of APE was fixed at > 12% decrease in ITI from baseline as described in our methodology. The patients were divided into one group that received standard treatment after the appearance of clinical signs of APE without considering the prediction of APE by EGM devise (Group 1), and another group of asymptomatic patients in whom development of APE was predicted by using only EGM measurements (Group 2). The latter participants' PT consisted of furosemide, intravenous nitroglycerine and supplemental oxygen.

RESULTS

One-hundred and fifty patients with acute STEMI were enrolled into this study. Group 1 included 100 patients (53% males, age 64.1 ± 12.6 years). Treatment was started after the clinical appearance of overt signs of APE. Group 2 included 50 patients (54% males, age 65.2 ± 11.9 years) who received PT based on EGM measurements. Group 2 had significantly fewer cases of APE (n = 4, 8%) than Group 1 (n = 100, 100%) (P > 0.001). While APE was lethal in six (6%) Group 1 patients, PT resulted in prompt resolution of APE in all four (8%) Group 2 patients.

CONCLUSION

ITI is a useful modality for early diagnosis and PT of pulmonary edema of cardiogenic origin.

Serum inter-cellular adhesion molecule 1 is an early marker of diagnosis and prediction of severe acute pancreatitis

AIM: To determine if serum inter-cellular adhesion molecule 1 (ICAM-1) is an early marker of the diagnosis and prediction of severe acute pancreatitis (SAP) within 24 h of onset of pain, and to compare the sensitivity, specificity and prognostic value of this test with those of acute physiology and chronic health evaluation (APACHE) II score and interleukin-6 (IL-6).

METHODS: Patients with acute pancreatitis (AP) were divided into two groups according to the Ranson’s criteria: mild acute pancreatitis (MAP) group and SAP group. Serum ICAM-1, APACHE IIand IL-6 levels were detected in all the patients. The sensitivity, specificity and prognostic value of the ICAM-1, APACHE IIscore and IL-6 were evaluated.

RESULTS: The ICAM-1 level in 36 patients with SAP within 24 h of onset of pain was increased and was significantly higher than that in the 50 patients with MAP and the 15 healthy volunteers (P < 0.01). The ICAM-1 level (25 ng/mL) was chosen as the optimum cutoff to distinguish SAP from MAP, and the sensitivity, specificity, positive predictive value, negative predictive value (NPV), positive likelihood ratio and negative likelihood ratio were 61.11%, 71.42%, 0.6111, 0.7142, 2.1382 and 0.5445, respectively. The area under the curve demonstrated that the prognostic accuracy of ICAM-1 (0.712) was similar to the APACHE-IIscoring system (0.770) and superior to IL-6 (0.508) in distinguishing SAP from MAP.

CONCLUSION: ICAM-1 test is a simple, rapid and reliable method in clinical practice. It is an early marker of diagnosis and prediction of SAP within the first 24 h after onset of pain or on admission. As it has a relatively low NPV and does not allow it to be a stand-alone test for the diagnosis of AP, other conventional diagnostic tests are required.